



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "levit"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=[128, 256, 384]						,	SCREAMING_SNAKE_CASE__=[4, 8, 12]						,	SCREAMING_SNAKE_CASE__=[4, 4, 4]						,	SCREAMING_SNAKE_CASE__=[16, 16, 16]						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=[2, 2, 2]						,	SCREAMING_SNAKE_CASE__=[2, 2, 2]						,	SCREAMING_SNAKE_CASE__=0.0_2						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)
				A__											=					image_size
				A__											=					num_channels
				A__											=					kernel_size
				A__											=					stride
				A__											=					padding
				A__											=					hidden_sizes
				A__											=					num_attention_heads
				A__											=					depths
				A__											=					key_dim
				A__											=					drop_path_rate
				A__											=					patch_size
				A__											=					attention_ratio
				A__											=					mlp_ratio
				A__											=					initializer_range
				A__											=					[
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     version.parse("1.11"      )


			@property
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)







			@property
			def   snake_case__	(      self		)      ->	float:
				return 1e-4









'''simple docstring'''



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer
@require_tf
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													self.assertEqual(len(lowerCAmelCase__					)					,     len(lowerCAmelCase__					)					)
													for a, b in zip(lowerCAmelCase__					,     lowerCAmelCase__					):
																				self.assertAlmostEqual(lowerCAmelCase__					,     lowerCAmelCase__					,     delta=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      GradientAccumulator()
													accumulator([tf.constant([1.0, 2.0]					)]					)
													accumulator([tf.constant([-2.0, 1.0]					)]					)
													accumulator([tf.constant([-1.0, 2.0]					)]					)
													with self.assertRaises(lowerCAmelCase__					):
																				accumulator([tf.constant([1.0, 1.0]					), tf.constant([2.0, 2.0]					)]					)
													self.assertEqual(accumulator.step					,     3					)
													self.assertEqual(len(accumulator.gradients					)					,     1					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [-2.0, 5.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()					,     [0.0, 0.0]					,     tol=1e-2					)







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
													ops.enable_eager_execution_internal()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.config.list_physical_devices('''CPU'''					)
													if len(lowerCAmelCase__					) == 1:
																				tf.config.set_logical_device_configuration(
																				    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      tf.config.list_logical_devices(device_type='''CPU'''					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.distribute.MirroredStrategy(devices=devices[:2]					)

													with strategy.scope():
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GradientAccumulator()
																				SCREAMING_SNAKE_CASE       :   Dict         =      tf.Variable([4.0, 3.0]					)
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[int]         =      create_optimizer(5e-5					,     10					,     5					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      tf.Variable([0.0, 0.0]					,     trainable=lowerCAmelCase__					)

													def accumulate_on_replica(lowerCAmelCase__ :   List[str]					):
																				accumulator([gradient]					)

													def apply_on_replica():
																				optimizer.apply_gradients(list(zip(accumulator.gradients					,     [variable]					)					)					)

													@tf.function
													def accumulate(lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   List[Any]					):
																				with strategy.scope():
																											SCREAMING_SNAKE_CASE       :   Optional[Any]         =      strategy.experimental_local_results(lowerCAmelCase__					)
																											local_variables[0].assign(lowerCAmelCase__					)
																											local_variables[1].assign(lowerCAmelCase__					)
																											strategy.run(lowerCAmelCase__					,     args=(gradient_placeholder,)					)

													@tf.function
													def apply_grad():
																				with strategy.scope():
																											strategy.run(lowerCAmelCase__					)

													def _check_local_values(lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   int					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      strategy.experimental_local_results(accumulator._gradients[0]					)
																				self.assertListAlmostEqual(values[0].value()					,     lowerCAmelCase__					,     tol=1e-2					)
																				self.assertListAlmostEqual(values[1].value()					,     lowerCAmelCase__					,     tol=1e-2					)

													accumulate([1.0, 2.0]					,     [-1.0, 1.0]					)
													accumulate([3.0, -1.0]					,     [-1.0, -1.0]					)
													accumulate([-2.0, 2.0]					,     [3.0, -2.0]					)
													self.assertEqual(accumulator.step					,     3					)
													_check_local_values([2.0, 3.0]					,     [1.0, -2.0]					)
													apply_grad()
													self.assertListAlmostEqual(variable.value()					,     [4.0, 3.0]					,     tol=1e-2					)
													accumulator.reset()
													self.assertEqual(accumulator.step					,     0					)
													_check_local_values([0.0, 0.0]					,     [0.0, 0.0]					)




